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Batch Normalization is a key component in almost all state-of-the-art imageclassifiers, but it also introduces practical challenges: it breaks theindependence between training examples within a batch, can incur compute andmemory overhead, and often results in unexpected bugs. Building on recenttheoretical analyses of deep ResNets at initialization, we propose a simple setof analysis tools to characterize signal propagation on the forward pass, andleverage these tools to design highly performant ResNets without activationnormalization layers. Crucial to our success is an adapted version of therecently proposed Weight Standardization. Our analysis tools show how thistechnique preserves the signal in networks with ReLU or Swish activationfunctions by ensuring that the per-channel activation means do not grow withdepth. Across a range of FLOP budgets, our networks attain performancecompetitive with the state-of-the-art EfficientNets on ImageNet.

Characterizing signal propagation to close the performance gap in unnormalized ResNets